Abstract

Purpose This study aims to propose a speed guidance model of the CV environment to alleviate traffic congestion at intersections and improve traffic efficiency. By introducing the theory of moving block section for high-speed train control, a speed guidance model based on the quasi-moving block speed guidance (QMBSG) is proposed to direct platoon including human-driven vehicles and connected vehicles (CV) through the intersection coordinately. Design/methodology/approach In this model, the green time of the intersection is divided into multiple block intervals according to the minimal safety headway. Connected vehicles can pass through the intersection by following the block interval using the QMBSG model. The block interval is assigned dynamically according to the traveling relation of HV and CV, when entering the communication range of the intersection. To validate the comprehensive guidance effect of the proposed model, a general evaluation function (GEF) is established. Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Findings Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Also, compared to the single intersection speed guidance model, the GEF value of the QMBSG model improves over 17.1%. To further explore the guidance effect, the impact of sensitivity factors of the CVs’ environment, such as intersection environment, communication range and penetration rate (PR) is analyzed. When the PR reaches 75.0%, the GEF value will change suddenly and the model guidance effect will be significantly improved. This paper also analyzes the impact of the length of block interval under different PR and traffic demands. It is found that the proposed model has a better guidance effect when the length of the block section is 2 s, which facilitates traffic congestion alleviation of the intersection in practice. Originality/value Based on the aforementioned discussion, the contributions of this paper are three-fold. Based on the traveling information of HV/CV and the signal phase and timing plans, the QMBSG model is proposed to direct platoon consisting of HV and CV through the intersection coordinately, by following the block interval assigned dynamically. Considering comprehensively the indexes of mobility, safety and environment, a GEF is provided to evaluate the guidance effect of vehicles through the intersection. Sensitivity analysis is carried out on the QMBSG model. The key communication and traffic parameters of the CV environment are analyzed, such as path attenuation, PR, etc. Finally, the effect of the length of block interval is explored.

Highlights

  • Intersection is an influx and distribution node of traffic flow in urban road networks

  • All indicators of the quasi-moving block speed guidance (QMBSG) model improve more than 50%, while the single-intersection speed-guidance (SISG) model only improves the Average speed (AS) and Delay time (DT)

  • The Acceleration interference (AI) value of QMBSG model increases by 14.7% compared to the SISG model, the QMBSG model improves the vehicle performance on the Velocity continuity (VC)

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Summary

Introduction

Intersection is an influx and distribution node of traffic flow in urban road networks. © Qing Xu, Jiangfeng Wang, Botong Wang and Xuedong Yan. Published in Journal of Intelligent and Connected Vehicles. With the development of the connected vehicles (CV) technology, it has become a novel method to alleviate the congestion at intersections. One of applications of the CV technology in intersection congestion alleviation is to guide the approaching vehicles to pass through the intersection by the speed guidance model. Most of the speed guidance models under the condition of CV focus on the traveling efficiency of the vehicles passing through the intersection and failed to consider comprehensively the balance between mobility and safety.

Related work
Algorithm description
Description of guidance strategy The QMBSG model follows three assumptions
The implementation process of the QMBSG model is
Model evaluation
Formulation of the general evaluation function
Case study
Conclusion
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